Seasonal distribution of algal pigments in an enclosed harbour area in
Saltenfjorden, Norway, viewing possible impacts from hydrographic changes
May 2016
AK306F MSc in Aquaculture Ann Helen Haubakk
i
ii Acknowledgements
I would like to give a gratitude to my main supervisor Einar Skarstad Egeland and assistant supervisor Åge Mohus at the Faculty of Bioscience and Aquaculture at Nord University, for all the good help and support.
I would like to say thank you to my fellow students and colleagues at Mørkvedbukta research station, especially Morten Krogstad for his technical support.
A greeting I will give to my workplace at Nordland county council for giving me the opportunity to finish writing my paper. My colleagues Ketil Olsen and Elisabeth Karlsen deserve special thanks for their positive support.
I would like to thank Silje Forbord at SINTEF, Sissel Larsen and Merete Hestdal at
Norwegian Food Safety Authority, and also Geir Johnsen at NTNU, for all positive help. A credit also goes to The Norwegian Meteorological Institute (MET), for providing information and additional data sets.
Finally, but not least, my dear family deserves my thanks for their support and understanding.
Bodø 18.5.2016 Ann Helen Haubakk
iii Abstract
This work is meant to participate in building knowledge about different seasonal algal
pigments in a semi enclosed bay area in a fjord, as well as underlining the different aspects to be considered in algal cultivation.
This study summarises different theories regarding the most important mechanisms, including biotic and abiotic factors reported to have an effect on algal growth and composition. The algal components play an important role as primary producers in the oceans and also in commercial industry. The main focus of this assignment are the microalgal community.
Different methods for obtaining and processing seawater-samples for analyses are addressed.
The field sample analyses were performed in Mørkvedbukta, a bay-area in Saltenfjorden in Northern-Norway. It was performed probe measurements in the water column measuring water temperature, salinity, density, and fluorescence. The seawater samples was analysed using a HPLC-instrument with a special focus on specific algal pigments. Different datasets such as algal cell counting and meteorological measurements on cloud cover, air temperature and precipitation, was obtained from external sources.
During the experiment performed over a one-year period, there was found some possible correlations between the pigment concentration, the algal cell-count and fluorescent
measurements regarding the spring point of the main blooming period. The top periods for the different periods was always correlating.
It is found that these studies might support previous examinations of phytoplankton communities, regarding the timing of the spring bloom.
It was found indications of specific pigments dominating different seasonal periods. There was found possible correlations between the pigment analyses, algal cell counts, and
fluorescence held against the hydrographic- and meteorological measurements, which could indicate an influence on the pigment concentration, but tough in a larger seasonal scale.
iv Sammendrag
Dette arbeidet er ment å delta i å bygge kunnskap om ulike sesongmessige variasjoner i algepigmenter i et semi-lukket fjordområde, samt å understreke ulike aspekter som bør vurderes ved algedyrking.
Dette studiet oppsummerer ulike teorier om de viktigste mekanismene, herunder biotiske og abiotiske faktorer som er antatt å ha en effekt på algevekst og sammensetning. Betydningen av alge komponenter, både som en viktig primærprodusent i havet og til kommersielle formål.
Hovedfokus for denne oppgaven er mikroalgesamfunnet. Ulike metoder for oppnåelse og bearbeiding av sjøvannsprøver for analyse er også adressert.
Feltarbeidet ble gjort i Mørkvedbukta, ei bukt i Saltenfjorden, Nord-Norge. Det ble utført målinger i vannsøylen med en sonde som målte vanntemperatur, saltholdighet, tetthet og fluorescens. Sjøvannsprøvene ble analysert i et HPLC-instrument, med spesielt fokus på bestemte algepigmenter. Ulike datasett fra algecelletelling, samt meteorologiske målinger av skydekke, lufttemperatur og nedbør ble innhentet fra eksterne kilder.
Ved dette eksperimentet, som ble utført over en ettårsperiode, ble det funnet enkelte mulige sammenhenger mellom pigmentkonsentrasjon, algecelletall og fluorescerende målinger, med tanke på selve vekstperioden. Maksimalmålingene for de ulike parameterne og for de ulike periodene var ikke alltid sammenfallende.
Det er dog funnet at disse studiene kan støtte tidligere undersøkelser av planteplankton samfunn angående tidspunktet for starten på våroppblomstringen.
Det ble funnet indikasjoner på at spesifikke pigmenter var dominerende i ulike perioder av sesongene. Det ble funnet mulige korrelasjoner mellom pigmentanalysene, algetellingene og fluorosens opp mot de de hydrografiske- og de meteorologiske målingene, noe som kan indikere en innflytelse på algeveksten, men da i en større sesongmessig målestokk.
v Index
Acknowledgements ... ii
Abstract ... iii
Sammendrag ... iv
Index ... v
List of figures ... viii
List of tables ... x
1. Introduction ... 1
1.1 General background ... 1
1.2 Aims ... 2
1.3 Microalgae ... 2
1.3.1 Algae aquaculture ... 4
1.3.2 Morphological features of microalgae ... 4
1.3.3 Algae pigments ... 5
Chlorophylls ... 7
1.4 Interactions and different effects on phytoplankton growth and distribution ... 11
1.5 A view on climatic changes ... 13
1.6 Field and lab analyses algae – different methods ... 17
1.6.1 Chromatography ... 18
2 Materials and methods ... 20
2.1 Study site ... 20
2.2 Field sampling and hydrographic measurements ... 21
2.2.1 Hydrographic field measurements ... 21
2.2.2 Ocean water sampling ... 21
2.3 Sample preparations ... 22
2.3.1 Filtration and freezing ... 22
2.4 Extraction ... 22
2.5 HPLC analysis ... 23
2.6 Obtaining and processing external meteorological- and algal rapports ... 24
vi
2.7 Data reporting and statistical analyses ... 25
3 Results ... 26
3.1 Pigment analyses ... 26
3.1.1 Estimated total seasonal pigment distribution ... 26
3.1.2 Seasonal distribution of pigments ... 27
3.2 Hydrographic SD-measurements ... 35
3.2.1 Fluorescence ... 36
3.2.2 Seawater temperature ... 36
3.2.3 Oxygen ... 37
3.2.4 Salinity and density ... 38
3.3 Meteorology - Weather reports ... 40
3.3.1 Cloud cover ... 40
3.3.2 Precipitation ... 40
3.3.3 Air temperature ... 41
3.4 Processed algal reports ... 42
3.5 Combined results ... 43
3.5.1 Biomarkers - combined results ... 43
3.5.2 Hydrographic conditions and biomarkers - combined results ... 47
3.5.3 Meteorological conditions and biomarkers - combined results ... 49
4 Discussion ... 51
4.1 Technical and statistical considerations ... 51
4.2 Comparing different algal biomarkers ... 52
4.2.1 Pigment analyses, fluorescence and cell counts ... 52
4.3 Hydrographic measurements compared to algal biomarkers ... 54
4.3.1 Salinity and density ... 54
4.3.2 Seawater temperature ... 54
4.4 Meteorological measurements compared to algal biomarkers ... 55
4.4.1 Irradiation influences, cloud cover, air temperature and biomarkers ... 55
vii
4.5 Possible multiple combined effects ... 55
4.6 Future considerations ... 56
5. Conclusions ... 56
References/Bibliography ... 58
APPENDIX A SD-Measurements ... 69
APPENDIX B; HPLC ... 73
APPENDIX C Meteorological reports ... 98
APPENDIX D Algal cell counts ... 101
APPENDIX E Pigment abbreviations ... 102
viii List of figures
Figure 1. Sketch illustration of diatoms. ... 3
Figure 2. Algal example structure. ... 5
Figure 3. Structures of common algal carotenoids. ... 9
Figure 4. Global average air temperature abnormalities. ... 13
Figure 5. Temperature deviations in Northern-Norway. ... 13
Figure 6. Temperature winter-trend Northern Norway. ... 14
Figure 7. Spring-temperature deviations in Northern Norway. ... 14
Figure 8. Temperature summer-trend Northern Norway. ... 15
Figure 9. Temperature autumn-trend Northern Norway. ... 16
Figure 10. Precipitation trends in Norway. ... 17
Figure 11. Destination map. ... 20
Figure 12. Sampling destination site picture. ... 21
Figure 13. Seasonal distribution of total pigments- monthly averages ... 26
Figure 14. Total pigment concentration – Daily averages. ... 27
Figure 15. Chlorophyll a. ... 28
Figure 16. Chlorophyll b. ... 29
Figure 17. Fucoxanthin. ... 29
Figure 18.Cryptoxanthin. ... 30
Figure 19. Diadinoxanthin and diatoxanthin. ... 31
Figure 20. Crocoxanthin. ... 31
Figure 21. Zeaxanthin. ... 32
Figure 22. Neoxanthin. ... 32
Figure 23. β,β-carotene. ... 33
Figure 24. Prasinoxanthin. ... 33
Figure 25. Lutein. ... 34
Figure 26. Violaxanthin and antheraxanthin. ... 35
Figure 27. Fluorescence. ... 36
Figure 28. Seawater temperature. ... 37
Figure 29. Oxygen levels. ... 37
Figure 30. Salinity and density. ... 38
Figure 31. Salinity vs. density. ... 39
ix
Figure 32. Cloud cover. ... 40
Figure 33. Precipitation. ... 41
Figure 34. Air temperature. ... 42
Figure 35. Algal cell counts. ... 43
Figure 36. Combined biomarkers. ... 44
Figure 37. Distribution of dominant pigments. ... 45
Figure 38. Chlorophyll a and algal cell counts. ... 46
Figure 39. Seasonal distribution of pigments involved in the Xanthophyll cycle. ... 47
Figure 40. Hydrography and biomarkers. ... 48
Figure 41. Meteorology and biomarkers. ... 50
x List of tables
Table 1. Division and class distribution of some common algal pigments. ... 6
Table 3 HPLC solvents gradients. ... 23
Table 4. Table SD-results January-March 2010. ... 69
Table 5. SD-results April-June 2010. ... 70
Table 6. SD-results July-August 2010. ... 71
Table 7. SD-results September-November 2010. ... 72
Table 8. HPLC - Result-sheets January 2010. ... 73
Table 9. HPLC- Result-sheets January 2010 cont. ... 74
Table 10. HPLC - Result-sheets February-March 2010. ... 75
Table 11. HPLC - Result-sheets April 2010. ... 76
Table 12. HPLC - Result-sheets April 2010 cont. ... 77
Table 13. HPLC - Result-sheets May 2010. ... 78
Table 14. HPLC - Result-sheets May 2010 cont. ... 79
Table 15. HPLC - Result-sheets May 2010 cont. ... 80
Table 16. HPLC - Result-sheets May 2010 cont. ... 81
Table 17. HPLC - Result-sheets May 2010 cont. ... 82
Table 18. HPLC - Result-sheets June 2010. ... 83
Table 19. HPLC - Result-sheets June 2010 cont. ... 84
Table 20. HPLC - Result-sheets June 2010 cont. ... 85
Table 21. HPLC - Result-sheets June 2010 cont. ... 86
Table 22. HPLC - Result-sheets July 2010. ... 87
Table 23. HPLC - Result-sheets July 2010 cont. ... 88
Table 24. HPLC - Result-sheets July 2010 cont. ... 89
Table 25. HPLC - Result-sheets July 2010 cont. ... 90
Table 26. HPLC - Result-sheets August 2010. ... 91
Table 27. HPLC - Result-sheets August 2010 cont. ... 92
Table 28. HPLC - Result-sheets August 2010 cont. ... 93
Table 29. HPLC - Result-sheets August 2010 cont. ... 94
Table 30. HPLC - Result-sheets August 2010 cont. ... 95
Table 31. HPLC - Result-sheets September-October 2010. ... 96
Table 32. HPLC - Result-sheets November-December 2010. ... 97
Table 33. Cloud cover, precipitation and air temperature from January-May. ... 98
xi
Table 34. Cloud cover, precipitation and air temperature from May-September. ... 99
Table 35. Cloud cover, precipitation and air temperature from September-December. ... 100
Table 36. Algal cell counts given in number of cells. ... 101
Table 37. Recommended abbreviations. ... 102
1 1. Introduction
1.1 General background
The sea holds tremendous amounts of life forms and many crucial and useful resources for the process industry, medicine technology, and specially food industry. Algae are regarded a very important part of this ecosystem, with a wide range of varying species. The ecosystems of sheltered areas, shows differences compared to more exposed areas. Divisions and species also vary by season and latitude.
It is useful to look at the benefits, as well the disadvantages that algae growth can provide. In aquaculture development today it is now an ongoing process of cultivating different
microalgae cultures in containers, and macroalgae on sites in fjords. Different components in both the micro- and macroalgal cells, can be used for commercial purposes, hereunder in combination with the fish food industry, bio-fuel etc. Locally in Nordland, it is of interest to find out more about local wild grown algal species, to replace imported culture media.
Particularly the sheltered areas, as the microalgal-aquaculture are produced in relative closed areas or in containers.
Facing global environmental challenges, the importance of phytoplankton primary production is essential, as to binding and transforming large amounts of carbon dioxide. A diversity of processes occurs in algal cells, aimed to adapt different environmental challenges.
Algal cell pigments are essential components in the photosynthetic process. They serve as light-harvesting pigments or as accessory pigments, enhancing light absorption at specific wavelengths. The accessory pigments might also reduce cell damage caused by intensive light, by exporting excess heat energy. These adjustment processes might take seconds or as the large acclimating adaptations might take several years.
Analysing pigment concentration of seawater samples might provide information about the algal components present in the water column. Pigments specific for certain algal groups, can be used indicators, and Chlorophyll a content might give an estimate on the total algal
biomass. Based upon their compositional differences several methods are be used to classify or identify them, combining methods provides more certain data.
2 1.2 Aims
The purpose of this project was finding possible seasonal variations in the distribution of naturally occurring algal pigments in seawater collected in a shallow water column.
It was aimed to find possible correlations between detected pigments and fluorescence levels in present study held against relevant algal cell counting performed by others.
There were also examined if there could be found patterns of influences from hydrographic- and meteorological measurements, such as temperature, density, salinity and oxygen in the water column, and weather conditions such as temperature, cloud cover and precipitation.
Theory
1.3 Microalgae
Algae are a group of organisms that are rather difficult to place in the Kingdoms of Nature.
Placed along with the Protista, might ignore their photosynthetic abilities. Their
photosynthetic abilities might link them to land plants, and also the domain of bacteria even though Cyanobacteria, are no longer called algae (Larkum, 2003).
From many symbioses within heterotrophic hosts throughout time, the variety of different microalgal pigments evolved. Different algae with multiple abilities to make use of different lights withholding different wavelengths, being able to live within the different depths and adjustments in seasonal variations in light supply. These changes in adaptive composition might take seconds or days (Brunet et al., 2011).
When facing different radiances of light, the cells has the ability to do calibrated internal changes in processes such as photosynthesis, respiration, growth-rate and also divisions (Brunet et al., 2011, Egeland, 2016, Herzig and Dubinsky, 1993, Anning et al., 2000, Raven and Geider, 2003).
The marine phytoplankton makes out at least one fourth of the total vegetation on the planet.
They make up the baseline of primary production in the world`s oceans, and so affects all the levels of life up the levels of the aquatic food pyramid (Aiken et al., 2009).
The plankton production gives ocean zoo-species different colours important for camouflage and curtsies etc. The levels of oxygen in the water column, is also an important factor in
3 sustaining life in the water column. The prime producer helps oxygenating the water masses, as well as playing an important role as a food source (Aiken et al., 2009).
The spring bloom usually takes place in April-May in higher latitudes, often dominated by diatoms, and smaller amounts dinoflagellates (Carstensen et al., 2015, Huseby, 2002, Degerlund and Eilertsen, 2010). Bloom-periods that are dominated by chlorophytes and cyanobacteria are more common in areas of low salinity and higher temperatures (Carstensen et al., 2015).
Degerlund and Eilertsen (2010), summarises the quantitatively most abundant species from the spring bloom of the north Norwegian coast, and the Barents Sea along the northern to be the prymnesiophyte, Phaeocystis pouchetii and the cold water to temperate diatoms
Chaetoceros socialis, Skeletonema costatum sensu lato, Fragilariopsis oceanica,
Thalassiosira spp., Chaetoceros furcellatus, Chaetoceros compressus, Chaetoceros debilis and Bacterosira bathyomphala. Diatoms and Phaeocystis abundance varies highly.
P. pouchetii are found during all stages of the spring bloom and are sometimes completely dominating (Degerlund and Eilertsen, 2010). Figure 1 gives an illustration of varieties in shapes of diatoms.
Figure 1. Sketch illustration of diatoms.
A Sketch illustration of diatoms many different shapes( from WmC via Gemini.no (Ekra, 2011))
4 1.3.1 Algae aquaculture
Commercially growth of algal cultures are still increasing in popularity because of the micro algal many functional abilities and high valued contents, (Brunet et al., 2011)such as
carotenoids and fatty acids. This includes human food, animal feed products, wastewater treatments (Oswald, 1988a, Chen et al., 2016) and agriculture soil conditioners (Metting, 1988, Tseng, 2003). The algal species Skeletonema spp. , Tetraselmis suecica and
Thalassiosira pseudonana are frequently grown as aquaculture feed (Tseng, 2003), others for instance Dunaliella spp. (β, β-carotene) and Haematococcus pluvialis (astaxanthin), are grown as for their ability to synthesize carotenoids (Dufossè, 2009).
Many algal pigment, and other components might also be used in cosmetics (Wang et al., 2015), pharmaceutical industry, disease preventers, and dietary supplements (Pangestuti and Kim, 2011). As for commercial production especially pigments such as, β, β-Carotene, canthaxanthin, astaxanthin, lutein, lycopene and zeaxanthin can be mentioned (Mortensen, 2009). For instance has zeaxanthin been found to play an important role in preventing age- related macular degeneration (AMD) (Sajilata et al., 2008).
In large scale production it is a requirement that the systems are shallow to assure sufficient light exposure, usually performed in large ponds (Oswald, 1988b), in tanks or microalgae plant facilities (Nurra et al., 2014), that allows temperature and light regulations.
With the view on future aspects regarding climate change, and limitations in existing resources, there are also focus upon finding new sustainable, non-fossil sources of energy (Fedoroff et al., 2010). In this matter of concern, extremophilic microalgae for use in biotechnology have also been examined (Varshney et al., 2015, Stetter, 1999).
1.3.2 Morphological features of microalgae
The size and composition of microalgae varies highly, but there are some basic features that can be mentioned are thin rigid cellulosic cell wall, eukaryotic nucleus with pores, chromatin, nucleolus, karyolymph and lamellar, discoid or tubular cristae shaped mitochondria, and chloroplasts having an enclosed thylakoid as a place of photosynthesis. In addition to this some have flagella and proteinaceous (pyrenoid) for starch synthesis and storage (Singh et al., 2014). Figure 2, gives the composition of the Chlamydomonas reinhardtii cell, presented by Singh et al. (2014).
5
Figure 2. Algal example structure.
A sketch illustration of the algae Chlamydomonas reinhardtii cell (Singh et al., 2014).
1.3.3 Algae pigments
Pigments in algae include chlorophylls, carotenoids and phycobilins. They reflect at certain wavelengths of light and radiance different colours.
Pigments are considered important because if their function in the bio-system, but they can also benefit as natural food ingredients and others purposes, as mentioned in sub-chapter 1.3.1.
Pigments in phytoplankton are, according to Johnsen et al. (2011), usually revived as reaction centre pigments of photosystems I (PSI) and II (PSII), in the process of harvesting light as phytoplankton light-harvesting pigments (LHP) or photo-protective carotenoids (PPC).
Absorbing different colours of the light-spectra, LHP uses inductive resonance transferring light energy to the photosystems that induce electron flow, allowing the production of
reducing power (NADPH) and chemical energy (ATP) in the cell (Johnsen et al., 2011). The energy transfer are found very efficient (Johnsen et al., 2011, Govindje, 1995, Green et al., 2003, Larkum, 2003). Photo-protective carotenoids (PPC) also have light-harvesting functions, but the efficiency of these carotenoids are lower (Johnsen et al., 2011).
In the peripheral LHC of PSII, the bound carotenoids are heterogeneous depending on their classes (Takaichi, 2011).
6 Many species of phytoplankton has the ability to adjust carotenoids in their cells to make photosynthesis more efficient when exposed to certain light regimes, and they also creates photo-protective pigments aimed to minimalize cellular damage by excess radiation (Higgins et al., 2011, Falkowski, 2007).
Some pigments are considered division or class specific signal pigments, others more widespread among the divisions or classes. Table 1 gives a brief overview of the most common carotenoids distribution in algae. More detailed information and specifications regarding the abundance among the species are complex. Many pigments occur as trace elements in some algae, and they might be more dominating in others, or crossing divisions, some classes also has groups or species with varying pigment compositions. For a more thorough overview and worksheets, see for instance Egeland (2016), Wright (2005), Liaaen- Jensen and Egeland (1999) or Jeffrey et al. (2011).
Table 1. Division and class distribution of some common algal pigments.
Brief overview of some common algal pigments and a rough survey of their abundance among different algal divisions or classes (Takaichi, 2011).
7 1.3.3.1 Chlorophylls, carotenoids and phycobilins
Chlorophylls
The green reflecting chlorophyll pigments are the most abundant photosynthetic pigments in nature. Their molecular structure contains cyclic tetrapyrroles and they generally contain a central magnesium ion. As chlorophylls often generate toxic reactive oxygen species, because of their ability to maintain long-lived excited states, which can cause cellular damage, they often generate radicals during bright light conditions. Chlorophyll molecules, that are bound to proteins, serve as light receivers, and photo enzymes makes up the reaction centre (Green et al., 2003). Chlorophylls have the ability to function within the reaction centre performing a charged separation across the cell membrane.
Chlorophyll a makes photosynthesis possible, as by passing its energized electrons on to molecules participating in creating sugars. The pigment exists in all algae cells and
cyanobacteria with the ability to photosynthesize. DV-Chl a concentration might, for instance give a rough index for total prochlorophyte biomass in the ocean water (Wright et al., 1996, Higgins et al., 2011).
Chlorophyll b are present in green algae, some of the prochlorophytes (Egeland et al., 2011) and frequently found in prasinophytes (Barlow et al., 2016).
Chlorophyll cx exists in different forms with polar or non-polar structures. Some structures of non-polar chlorophyll c pigments are found in dinoflagellates (Barlow et al.) and haptophytes, though the structure of chl cx differs among to species or class. (Higgins et al., 2011).
According to Higgins et al. (2011), polar chlorophyll cx pigments such as MgDVP, occur as trace pigments in just about all taxa, but are considered a useful marker to detect
prasinophytes type 3. Others such as polar c1,c2 and c3 are more generally found among the chromophytes, such as diatoms, haptophytes and chrysophytes (Higgins et al., 2011).
One structural difference to mentioned are between Chl a and b, are the connected aldehyde group (-CHO) positioned at the third carbon where Chl a has the connected methyl group (- CH3).
8 Carotenoids
Carotenoids might be considered accessory pigments to chlorophyll in the light-harvesting antenna within algal cells. The colour of carotenoids usually varies from red, orange and yellow (Liaaen-Jensen and Egeland, 1999). Along with phycobilins carotenoids help absorb energy in the “green gap” near 500 nm.
Carotenoids are isoprenoid compounds with different structures that gives potential to gain or lose electrons easily, enabling absorption of photons and transferring excitation energy (Britton, 1995).
Among carotenoids are for instance carotenes (β,β-carotene, β,ε-carotene), simple carotenols (zeaxanthin, lutein),epoxides (violaxanthin, diadinoxanthin), acetylenic (diatoxanthin, alloxanthin, heteroxanthin),C37- skeletal (pyrrhoxanthin, peridinin), allenic (neoxanthin, vaucheriaxanthin, fucoxanthin), ketones (echinenone, canthaxanthin, astaxanthin, siphonaxanthin, prasinoxanthin) and glycosidic (myxoxanthophyll) carotenoids (Liaaen- Jensen and Egeland, 1999).
Some are more or less connected to specific algal divisions or classes such as; alloxanthin (Cryptophyta); fucoxanthin (Chrysophyceae, Raphidophyceae, Bacillariophyceae,
Phaeophyceae and Haptophyta); diadinoxanthin and vaucheriaxanthin (Xanthophyceae);
violaxanthin and vaucheriaxanthin (Eustigmatophyceae); peridinin (Dinophyta);
diadinoxanthin (Euglenophyta); siphonaxanthin (Chlorophyceae and Ulvophyceae); lutein (chlorophytes, chlorarachniophytes, prasinophytes, mesostigmatophytes); violaxanthin (chrysophytes, eustigmatophytes, synurophyses, mesostigmatophytes, chlorophytes prasinophytes, among others) and neoxanthin (chlorophytes, prasinophytes, among others (Takaichi, 2011, Jeffrey et al., 2011, Egeland et al., 2011).
It is important to keep in mind that many carotenoids are not micro algal specific and might be naturally occurring in for instance macroalgae just as well as in terrestrial land plants and other phototrophs. As carotenoids are pigments synthesized by photosynthetic organisms, and also a number of non-photosynthetic fungi and bacteria (Britton, 1995, Sajilata et al., 2008).
Among the microalgal community alone, there are more than one hundred known carotenoids (Liaaen-Jensen and Egeland, 1999), involving many different structures.
9 Figure 3 gives examples of common carotenes that are common in algal cells.
Figure 3. Structures of common algal carotenoids.
Among many different structures in algal carotenoids, some of the most common carotenoids as presented by Takaichi (2011).
Different groups in the structures of carotenoids and chlorophylls, provides different abilities.
For instance the keto groups at C-8 of fucoxanthin, siphonaxanthin and prasinoxanthin for example, which are found exclusively in algae, are reported to might having light-harvesting functions (Takaichi, 2011).
Another example are the water-soluble peripheral LHC of peridinin-chlorophyll-protein (PCP) isolated from Amphidinium carterae (Dinophyta) has a trimeric structure, and the monomer contains eight peridinin and two chlorophyll a molecules (Takaichi, 2011).
Carotenoids are, in most divisions, found in the reaction-centre complexes (RC) and the light- harvesting complexes (LHC) of photosystem I (PSI) as well as the RC and the core LHC of photosystem II (PSII), with the exception of zeaxanthin that is presented in some red algae of the LHC of PSI (Takaichi 2011). The cytochrome b6f complexes of the green alga
Chlamydomonas reinhardtii (Figure 2.) contain two β,β-carotene and two chlorophyll a molecules carotenoids that might have protective functions. As found β,β-carotene in both RC
10 might have protective functions, and carotenoids in the peripheral LHC of PSII mainly might have light-harvesting functions.
Epoxidation, de-epoxidation and the xanthophyll cycles
Important cell mechanisms for quick response to different light intensities are the processes of the xanthophyll cycles. Lohr (2011) refers to three major xanthophyll cycles; the
violaxanthin/antheraxanthin/zeaxanthin cycle (epoxidizing by zeaxanthin epoxidase (ZEP), during high light conditions, and reverted by violaxanthin de-epoxidase (VDE), the
diadinoxanthin/diatoxanthin and the lutein-epoxide/lutein cycle (Lohr, 2011, La Rocca et al.), whereas the last one only has been reported to exist in land plants. (Lohr, 2011, García-
Plazaola et al., 2007) According to García-Plazola et al. (2007), at least six xanthophyll cycles has been proposed to exist, where of four in algae.
The diadinoxanthin cycle occurs in Heterokontophyta, Haptophyta and Dinophyta, which contain diadinoxanthin and diatoxanthin.
Phycobiliproteins
Phycobiliproteins are light-harvesting pigments found in cyanobacteria, red algae, glaucocystophytes and cryptophytes (Zhao et al., 2011), where they help to optimise the photosynthetic processes in phytoplankton (Sobiechowska-Sasim et al., 2014).
Often consisting of a linear tetrapyrrolic chromophores, bilins, bound covalently to cysteins of apoproteins. They harvest light more efficient in the ‘green gap’, at wavelengths where
chlorophyll do not absorb light (Zhao et al., 2011, Sidler, 1994), they also have the ability to regulate their harvesting abilities, for instance light acclimations by reigning intensity and light-quality and (Zhao et al., 2011, Grossmann et al., 1993) supplies of nutrients, specially carbon, nitrogen and sulphur (Tandeau de Marsac and Houmard, 1993).
Phycobiliproteins are located in Photosystem II, and includes former names such as allophycocyanins (blue-green), phycobilliproteins (blue) and phycoerythrins (red) (Sidler, 1994). Now it is common to use prefixes such as C-,(cyanobacterial pigments), M-
(biliproteins from marine cyanobacteria), R-(red-algal biliproteins) B- /b (red algae
Bangiales), or they might also be characterized according to their light absorption maximum (Zhao et al., 2011, Glazer and Wedemayer, 1995). As the light produced by their fluorescence is distinctive and these can be used as bio-markers, especially detecting cyanobacteria
(Sobiechowska-Sasim et al., 2014).
11 1.4 Interactions and different effects on phytoplankton growth and distribution
There are multiple factors triggering the growth and decline of primary production. Some of the most important factors that influencing phytoplankton blooms are pulsed inputs of nutrients from river inflow, costal upwelling, atmospheric deposition, wind-induced entrainment of bottom water and neap-spring variability of tidal mixing and stratification (Carstensen et al., 2015). Wind enhancing water retention, heatwaves, stratification, increased retention time in flushed systems, benthic grazing pressure and changes in temperature and solar radiation are also important factors (Carstensen et al., 2015).
Shoreline coastal locations tend to show different seasonally qualitative and quantitative differences in algal communities (Metaxatos and Ignatiades, 2002).
The dynamics of stratified and partly mixed estuaries show recognizable patterns, on the other hand dynamics in shallow estuaries seem to be more variable and difficult to comprehend (Mann, 2006).
Temperature
Temperature effects might influence the timing of annual spring blooms in temperate waters.
In rate of growth, physiological processes, internal chemical compositions and also the entire composition of species in the pelagic phytoplankton communities (Lassen et al., 2010).
Lassen et al. (2010) found that a water temperature shift of only 3○C had an effect on the composition of the phytoplankton community. Although there has been many experiments conducted on temperature effect on phytoplankton communities, most have been carried out on lab monocultures (Lassen et al., 2010).
Irradiance
Changes in cloud cover other aerosols can cause big changes in total influx of UV-radiation.
Clouds attenuate UV, but even light broken cloud cover leads to an increased insolation (Diaz et al., 2000, Estupiñán et al., 1996).
The cycles if photosynthetic algae growth, is dependent on varying day length in accordance to the site latitude (Diaz et al., 2000, Johnsen and Sakshaug, 200). The point where the photosynthesis equals, called the compensation irradiance (Ec), are usually estimated by calculating the time course of decreasing amounts of fluorescence-derived chlorophyll a at specific depths during spring blooms (Diaz et al., 2000). Ec seems to be difficult to measure (Marra, 2004).
12 As phototrophic algae are depended on the radiance of light entering the water column, the thickness of the ozone layer, especially controlling UVB spectra and cloud cover (Diaz et al., 2000, Huseby, 2002)
Freshwater inflow - salinity
In areas with periods of high freshwater run off, fresh water might flush away salt and the marine phytoplankton (Mann, 2006). Especially in estuaries, periods of high freshwater might away most of the phytoplankton, and in other periods, areas can turn into a salt-wedge estuary with high primary production (Rendell et al., 1997).
Both primary production and the secondary production can be affected by high freshwater inputs. The planktonic community might be affected, both in abundance and in composition, also by hydrodynamic conditions caused by freshwater inflow (Viličić et al., 2008, de Madariaga et al., 1992).
Thompson et al. (2008) found that in bay-areas, phytoplankton bloom first appeared in the shallow water and later in the deeper regions. This was consistent with low benthic grazing rates, relatively bright light conditions and high nutrient levels (Thompson et al., 2008).
Carstensen et al. (2015) did a research program on phytoplankton blooms in estuarine and coastal waters covering 86 costal sites in North America and Europe. This study included phytoplankton species counts, measurements of salinity, temperature, Secchi depth, nutrients and chlorophyll a concentrations. In this large study there was found similarities in the timing of the spring blooms, but otherwise there was not found any notable combined patterns regarding the frequencies of blooming-periods, that could consistently related to latitude, tides, temperature, salinity, depth of the water column, stratifications or nutrients (Carstensen et al., 2015).
There has been found possible correlations between grainsize of sediments and distribution of different algae (Lucas and Holligan, 1999).
The timing and success of phytoplankton blooms has been shown to have a major impact on the survival of zooplankton, and further affect recruitment of fish larvae for such as for the Atlantic cod (Gadhus morhua) and haddock (Melanogrammus aeglefinus) (Buckley and Durbin, 2006, Campana et al., 1989).
13 1.5 A view on climatic changes
The global air temperature has increased dramatically in later years. Changes and
abnormalities, over millennia and decades are shown in figure 4. The data are estimated by using latest analyses, named HadCRUT4.4 (Morice et al., 2012, Jones, 2016).
Figure 4. Global average air temperature abnormalities.
Data presented by Climatic Research Unit ©, University of East Anglia (Jones, 2016).
In Northern Norway similar trends are found, as can be seen in figure 5, presenting
temperature deviations from 1900-2015. The data from Northern Norway shows a strong rise in average temperatures, especially from the late 80`s.
Figure 5. Temperature deviations in Northern Norway.
Temperature values are given as deviations of trend normal for the region Northern Norway (MET, 2015f).
14 In the winter-period the temperatures has increased in the last decades, as can be seen in figure 6. According to this data the winter-temperatures has increased later years, but 2010, was cooler than normal.
Figure 6. Temperature winter-trend Northern Norway.
Temperatures are given as deviations of trend normal for the Northern-Norway in the winter-season from 1900- 2016 (MET, 2015d).
The trend seem show increased spring-temperatures, especially from the early 2000 and onwards (Figure 7). In 2010 the spring-temperatures was 0.5oC higher than trend normal in this region.
Figure 7. Spring-temperature deviations in Northern Norway.
Temperatures are given as deviations of trend normal for the Northern-Norway in the winter-season from 1900- 2016 (MET, 2015e).
15 The temperature deviations in summer temperatures from 1900-2016 in Northern Norway, are given in Figure 8. This data show tendencies of increase, especially from the early 2000 and onwards. In 2010 the summer temperatures was approximately 0.5oC lower than trend normal for this region, on the contrary national measurements was 0.4oC higher than trend normal (MET, 2011).
Figure 8.Temperature summer-trend Northern Norway.
Temperatures are given as deviations of trend normal for the Northern-Norway in the summer-season from 1900-2016 (MET, 2015c).
Autumn temperature-deviations in Northern Norway from 1900-2016, are given in Figure 9.
As the figure show, also the autumn periods in this region has become warmer in the last decades. In 2010 the temperatures was approximately a quarter of a degree lower than trend normal for this season (MET, 2011), but still quite colder than the pervious and following years.
16
Figure 9. Temperature autumn-trends in Northern Norway.
Observed measurements are given as oC deviations of trend normal for the region Northern Norway Average in the autumn-season from 1900-2016 (MET, 2015b).
In a national scale, the summer of 2010 was considered a wet year, so as ranked as number four on a scale going back to the year 1900 (MET, 2011). Figure 10 gives the precipitation for Northern Norway, showing that in this region, the precipitation for 2010 was below the estimated trend-normal. The tendency also for this region over the last decades shows
tendencies of increased amounts of precipitation. There are naturally large local variations in Northern Norway. As parts of Nordland also was above trend normal precipitation in 2010 (MET, 2011). Local measurements for regarding present study are given in Appendix C and in processed figures in section 3. Results.
17
Figure 10. Precipitation trends in Norway.
Calculated measurements are given as percent (%) of trend normal for the region Northern Norway (MET, 2015a).
1.6 Field and lab analyses algae – different methods
Information on the abundance and species composition can be enumerated by using different methods. One of these methods is based on light microscopy. According to Mackey et al.
(1996), this method requires using extensive time on sample preparation and counting to find valid counts. This is especially difficult for identifications of less abundant species, since many of these lack external morphological feature that are taxonomically useful (Mackey et al., 1996, Li et al., 1983, Platt et al., 1983). Many species are also very fragile and might be damaged during sample fixation (Li et al., 1983).
Another method in algae class determination from unknown multi cultures, is examining amounts of reserve polymer synthesized from photosynthetic processes. During this process algae can produce different reserve substances (Madigan and Martinko, 2006). According to Madigan et al. (2006), the group Dinoflagellate produce starch (α-1,4-glucan) and
Chlorophyta (Green algae) the same in addition to sucrose. Euglenophyta produce paramylon (β-1,2-glucan), Chrystophyta (Golden brown algae and diatoms) produce lipids. The brown algae (Phaeophyta) produce laminarin (β-1,3,-glucan and mannitol), while as red algae (Rhodophyta) produce floridian starch (α-1,4- and α-1,6-glucan) (Madigan and Martinko, 2006).
18 It is common to use spectrophotometric and spectrofluorometric methods identifying
pigments (Neveux et al., 2011). DNA-analyses can also be used to determine the algal cell conditions in a known culture media (Leu and Hsu, 2005), as to find limits of exposition tolerances and cell damage. It is also possible to study the photosynthetic membrane using force microscopy (AFM), (Liu and Scheuring, 2013) and x-Ray analyses of membrane protein structure (Feld and Frank, 2014). (Volent et al., 2011)
As for monitoring phytoplankton in fjords, satellite data and Ferry boxes might be useful in combination to algal cell counts and HPLC-pigment analyses (Volent et al., 2011).
A useful method for finding correlations between algal classes, based on specific signal pigments are the CHEMTAX-method (Mackey et al., 1996, Higgins et al., 2011). This method is aimed to estimate the contributions of different phytoplankton classes up against the pigment concentrations in water samples (Mackey et al., 1996).
Chromatography, especially HPLC-analyses are further addressed under sub-section 1.6.1.
1.6.1 Chromatography
Chromatography analysis are different techniques for separating components based on the distribution of the components between a moving liquid or gaseous phase and a solid stationary phase, that usually consists of a large surface area (Sharp, 2003).
HPLC is analyses are a preferred method when analysing pigments in cells, especially if the aim is to find amount contents in samples (Egeland, 2016, Garrido et al., 2011).
In the column that makes up the primary part of a HPLC system, where pigments are
separated for identification. Different pigments passes through a column at different retention times (tR) in accordance to their polarity and the polarity of the mobile phases. In the array the pigments are detected and presented in different peaks as a function of time (Bidigare et al., 2005). There are many different HPLC-systems available, and the choice of specifications and accessory equipment, must be done in accordance to the purpose and needs (Neeley et al., 2011).
For seawater for algae culture analyses in mesotrophic waters, it is recommended to filtrate from 1 to 2 litres of seawater per sample. Pre filtrating the cultures for removal of
zooplankton will also could exclude the chain forming phytoplankton, and is therefore not a recommended procedure for this purpose (Bidigare et al., 2005).
19 It is recommended to use positive pressure filtrating system so that larger amounts can be filtrated. It can be used different filters, but preferably Whatman GF/F glass fibre filters, with minimum 0.7 µm pore size are recommended to filtrate natural seawater (Bidigare et al., 2005). Oher studies has been performed using different pore sizes, and it was then found important to use filters to be able to trap particles that are at least 0.2 µm or smaller, or the results might give to low values (Li et al., 1983).
To minimalize the risk of degeneration of the pigment samples must never be exposed to bright light, high temperature, uncleaned environments, this to avoid changes in the pigment concentrations. The samples exposition to room temperature must be kept as short as possible, the samples are preferably kept in a deep freezer, and during the analysing process the
sampling bottles must be stores at 4○C in a vial compartment (Bidigare et al., 2005, Egeland, 2012).
20 2 Materials and methods
2.1 Study site
The study was performed at the research station Mørkvedbukta, run by Nord University in Bodø, Northern-Norway. The fieldwork were performed from January-December 2010 from a small floating dock located inside a semi enclosed loch (67´16,655´´N 14´ 33,390´E) (Figure 11).
Figure 11. Destination map.
The symbol (→●) marks the sampling site for seawater-material, SD-probe measurements, and algal cell counts.
The symbol (*) marks the meteorological station located near Bodø airport (Processed from ArcMap).
Close access to the research station enabled regular sampling regime. A molo-construction provides protection from the open fjord system of Saltenfjorden, as shown in figure 12.
21
Figure 12. Sampling destination site picture.
The arrow (→) marks the sampling site (Processed from ArcMap via OrthoPhoto).
2.2 Field sampling and hydrographic measurements
Samples of seawater were collected two or three times a week from December 2009 to December 2010. The samples were taken either in the morning or in the afternoon. The column was uneven and it was experienced depths between 8 and 9 m, due to different tides.
2.2.1 Hydrographic field measurements
Measurements of seawater conditions was performed using an CTD/STD Sound Vel. probe SD-204 equipment, frequently used and approved for scientific and commercial purposes.
The instrument measured salinity, oxygen, fluorescence, water temperature, depth, density and pressure. The instrument was set to register and save the results each minute.
Connected to a rope, the activated probe was slowly lowered towards the bottom, pulled back up, and immediately deactivated. The instrument was thoroughly cleaned using sterilized freshwater after each measurement. Measurements data then were transferred to the corresponding computer program (section 2.6).
2.2.2 Ocean water sampling
A flexible tube with a sized diameter of 10 cm, with a connecting rope and a stone, was used to collect ocean water. The tube was lowered into the water column, then by rising the lower end pulling the rope, to get an estimate of the water column from top to bottom.
Approximately 8 – 10 litres of seawater were collected in a bucket each time.
22 2.3 Sample preparations
Immediately after, the seawater was transported to the Wet-lab at the research station, for a vacuum filtration process. The filter samples were afterwards transported to the lab facilities at the university for further lab-processes.
2.3.1 Filtration and freezing
Seawater were filtrated through Whatman® glass GF/F 42.5 mm (47 mm from October samplings and onward) filters provided by Sigma-Aldrich (Sigma-Aldrich, 2009) in a water- driven vacuum filter array. An amount of 2 L seawater was carefully vacuumed through each filter, making sure that the filters did not dry completely. The filers were then folded, put into separate aluminium foil wrappings, and enclosed in zipper-bags. Four replicate filter samples were produced each day.
The samples were sealed in an expanded polystyrene-box placed in a deep freezer holding approximately minus 20oC, in a deep-freezer, at the wet-lab and at the University. All the equipment was thoroughly cleaned using sterilized water to avoid contaminations/inferences from unsterilized tap water.
2.4 Extraction
For the extraction process, the frozen filters were cut into squares of approximately 1 mm size and put into separate reagents tubes. Using ice-cold pre-mixture of 30 % methanol in
propanone, 5 mL were added to each tube, then top coated with nitrogen gas and a sealed with a tight lid. The tubes were kept in upright position at about minus 20oC in a deep freezer overnight, or for around 24 hours.
2 ml of each sample was pipetted into amber 2 ml HPLC-sampling vials, top flushed with nitrogen gas, capped and placed in a HPLC trey-compartment holding 4oC.
This procedure was performed on three filters of each selected day. As long time storage, and the fact that the set HPLC-program uses two hours per vial/sample, could degenerate the samples, different batches was processed throughout the year. Consecutively the data sets were evaluated, based upon quality or interest, and if needed, extra filter samples from parallel replicates and days in between were analysed. To examine if prolonged storage, or other factors, might have had an influence on the result, the parallel samples run at different analyses was compared.
23 2.5 HPLC analysis
The HPLC analyses was performed by using a Hewlett Packard Agilent 1100 HPLC instrument, equipped with a quaternary pump system, vacuum degasser, thermostatted auto sampler with enlarged injection loop, thermostatted column compartment and a diode array detector (Egeland, 2012). Specifications and instructions for use was found in; Agilent 1100 Series HPLC Value system User´s Guide (Agilent-Technologies, 1999).
The procedure was performed in accordance to the UN method, for analysing oceanographic field samples. The method is based on using two C18 columns and a lower solvent flow.
(Egeland, 2012).
Two identic C18 columns were used after each other (ACE 5 C18 part no. ACE-121-2546, 4.6x250 each, 5.0 µm pack) with a separate guard column (ACE), at a column temperature held at 25o C.
The auto sampler drew 50 µL from each sample extract. The injection flow was 0.5 mL/m and air temperature at 4oC in the vial tray compartment.
1 M ammonium acetate (AmAC), methanol (MeOH), acetone and hexane were used as eluants, specifications for gradients are given in table 2.
Table 2 HPLC solvents gradients.
Time given in minutes and solvents in percentage (%) of total amounts.
Time 1 M AmAc
(%)
MeOH (%)
Acetone (%)
Hexane (%)
≤0 20 80 0 0
60 0 70 30 0
100 0 30 50 20
110 0 0 40 60
120 0 100 0 0
130 20 80 0 0
Detection wavelengths for the chromatograms were 390 nm (Signal 1), 420 nm (Signal 2), 450 nm (Signal 3) and 480 nm (Signal 4).
24 The instrument had been pre-calibrated in accordance to method. Each analyse consisted of twelve vial samples, in addition to one control blank/non-sample vial.
It was briefly examined if prolonged storage might have had an influence on the results. This was performed by comparing data from the extra samples from the same batch, analysed at different days. Result sheets obtained was further processed and calculated into excel tables and figures (see section 2.6).
2.6 Obtaining and processing external meteorological- and algal rapports
The weather rapports used in this experiment were downloaded from the Meteorological Institute (MET), a national database. Data are distributed from their website, given credit, if used for scientific purpose. Specific information about daily measurements from 2010 was provided on request. The collected information also included the meteorological climate trends over millennia and decades, based on average normal weather conditions (section 1.8).
The institute has many different measuring stations. The station for was at Bodø airport observation station (82290) located about 11 metre above sea level, approximately 10 kilometres in westerly direction of the sampling site, was considered the most representative shoreline station (see figure 11 in section 2.1).
The processed datasets obtained from Meteorologist institute, such as precipitation, air temperature and estimated cloud cover was set in tables and processed. The data from 2010 were compared to the measured SD and HPLC-results.
Data obtained regarding climatic conditions of normal averages the past 100 years, and conditions from 2010 to 2014 is presented in section, 1.5.
Data from algal cell counting performed in the bay in 2010, was obtained from SINTEF.
Additional information and permission to use the data was also given from Norwegian Food Safety Authority, responsible for the connecting project. Data are published with permission.
The obtained raw data was processed and compared to the data from the presented study. The this sampling was, performed as described in Norwegian Standard (NS 9429:2007) according to one of the participants (Forbord, 2016).
The data from the cell counting was reported to be from collecting samples at approximately the same spot, from the floating dock (Larsen, 2016) as illustrated in figure 11 and 12.
25 2.7 Data reporting and statistical analyses
Results from probe measurements in the water column was uploaded, using software; Mini Soft SD 200 W for STD/CTD Sound Vel. Probe SD204, version 3.7.2.109, from SAIV AS Bergen, Norway (SAIV-A/S, 2009).
The results from the HPLC-analyses was obtained using the connecting computer program Value Solution Chem Station software, for 1100 series HPLC modules from Agilent
Technologies (Agilent-Technologies, 1999). The HPLC automatically calculated the amounts of pigments for the injected samples. The concentration in µg/L s.w. (as presented raw-data in tables) and mg/L s.w. (as presented in figures) of each pigment per litre seawater was
calculated. Calculating on the injected sample volume (50 µl), against the total volume of the sample, and the volume of seawater filtrated per sample (2 L).
Excel-work sheets were used for calculations, tables and graphic figures. Microsoft Office Excel 2010 for Windows was used to prepare all tables and graphs presented as results in this report. All result graphs presented are based on produced tables.
Full list of results from SD-probe measurements and HPLC-results are given in Appendixes A and B. The processed reports from meteorological reports and algal cell counts are presented in Appendix C and D respectively. Please note that not all measurements are given in
accordance to The international System of units (SI).
26 3 Results
The results are presented in different terms. Section 3.1, gives schematic dispositions of the pigment analyses, summarising the data presented in Appendix B. The hydrographic measurements and obtained meteorological data sets are given in section 3.2 and 3.3, based on data presented in Appendixes A and C. Section 3.4 gives figures processed from obtained algal count raw-data given in Appendix D, before the summarising disposition of selected combined results. Note the different scales and calculated translations.
3.1 Pigment analyses
The results presented in this section are based on HPLC- analyses of seawater samples from data given in Appendix B.
3.1.1 Estimated total seasonal pigment distribution
The concentration of monthly average pigment-concentration over the year-period is summarised in figure 13. The highest monthly average was in May with an estimated concentration of 2.5 mg total detected pigments per litre of seawater. The average monthly pigment distribution rises from April on to May, with a slow decline through the months onto October.
Figure 13.Seasonal distribution of total pigments- monthly averages
Calculated concentration per litre of seawater, based on average monthly values (Appendix B).
0 0,5 1 1,5 2 2,5 3
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
mg/ L s.w.
Pigment concentration - monthly averages
27 The distribution of total pigment distribution, given as based on average from each sampling day show a more fluctuating concentration pattern (Figure 14).
Figure 14. Total pigment concentration based on daily averages.
Pigment concentration based on average daily samples.
3.1.2 Seasonal distribution of pigments
Estimated concentrations of the most abundant pigments in 2010 are presented given
concentrations are milligrams per litre seawater. Numbers calculated from tables in appendix B, gives the monthly average. Note the different scales in the schemes.
28 3.1.2.1 Chlorophyll a
Concentration of chlorophyll a shows a rising peak from April, on to a steeper curve rising in May (Figure 15). The concentration drops drastically onto July and declining towards
September. Highest monthly average concentrations of Chlorophyll a was in May, measuring 1.9 mg/L s.w. In February and October, the concentration dropped more than by the half from its previous month, from 0.1 mg/L in January to 0.03 mg/L in February, and from 0.1 mg/L in September, to measuring 0.04 mg/L in October.
Figure 15. Chlorophyll a.
Average concentration of chlorophyll a during each month of 2010, given as mg/L seawater (Appendix B).
3.1.2.2 Chlorophyll b
Two distinct peaks of chlorophyll b can be observed, a small peak in May and a larger top in August, with a distinct drop in pigment concentration in June-July (Figure 17). From January to April the measurements varied from zero to 0.004 mg/L s.w., rising to a small top at 0.086 mg/L s.w. in May. Afterwards there was a decline in June at 0.026 mg/L and July at 0.033 mg/L. In August the concentration of Chlorophyll bwas at 0.15 mg/L s.w., before a downfall in September measurements at 0.022 mg/L. In October 0.007 mg/L, going on zero values for November and December.
29
Figure 16. Chlorophyll b.
Average concentration of chlorophyll b during each month of 2010, given as mg/L seawater (Appendix B).
3.1.2.3 Fucoxanthin
Fucoxanthin concentration maximum was in June this year, and was found in all months except December (Figure 17). January had a monthly average of 0.0019 mg/L seawater, with declining values in February and March at respectively 0.003 mg/L seawater and 0.006 mg/L s.w. In April at 0.08 mg/L and May 0.017 mg/L seawater. Top average concentration values was in June at 0.32 mg/L seawater, before declining to 0.2 in July and 0.075 mg/L seawater.
in August. From September- December, the concentration was at 0.047; 0.012; 0.001; 0 mg/L seawater respectively.
Figure 17. Fucoxanthin.
Average concentration of fucoxanthin during each month of 2010, given as mg/L seawater (Appendix B).
30 3.1.2.4 Cryptoxanthin
A small amount of cryptoxanthin was found from January to April, increasing to top-average concentrations in May (Figure 18). There were a steep decline downwards from May onto June, and then a slack curve further. The calculated average monthly concentration gives 0.17 mg/L s.w.in May. The slope curve towards September slack curve forms after a peak in May.
Figure 18.Cryptoxanthin.
Average concentration of cryptoxanthin during each month of 2010, given as mg/L seawater (Appendix B).
3.1.2.5 Diadinoxanthin and diatoxanthin
The concentration of diadinoxanthin and diatoxanthin shows a distinct curve in June, steeply rising and declining (Figure 19). Diatoxanthin was only found in samples from June. The top in June at maximal average of 0.028 mg/L.
31
Figure 19.Diadinoxanthin and diatoxanthin.
Average concentration of diadinoxanthin and diatoxanthin during each month of 2010, given as mg/L seawater (Appendix B).
3.1.2.6 Crocoxanthin
Maximum concentration of crocoxanthin was found in May this year, with a successive decline on to end of August (Figure 20). The pigment was present in January samples, and from March to August.
Figure 20.Crocoxanthin.
Average concentration of cryptoxanthin during each month of 2010, given as mg/L seawater (Appendix B).
32 3.1.2.7 Zeaxanthin
Zeaxanthin was only found in samples from January to October (Figure 21). Maximal amounts were in August, with maximal daily average of 0.103 mg/L seawater on August 11 (Appendix B).
Figure 21.Zeaxanthin.
Average concentration of zeaxanthin during each month of 2010, given as mg/L seawater (Appendix B).
3.1.2.8 Neoxanthin
Maximal amounts of neoxanthin were found in January and May, with average monthly concentrations with respectively of 0.034 mg/L and 0.028 mg/L seawater (Figure 23).
Figure 22. Neoxanthin.
Average concentration of neoxanthin during each month of 2010, given as mg/L seawater (Appendix B).
33 3.1.2.9 β,β-carotene
Highest concentration of β,β-carotene was in July with an average concentration of 0.033 mg/L seawater (Figure 23). The concentration levels started rising from April and onward.
Figure 23. β,β-carotene.
Average concentration of neoxanthin during each month of 2010, given as mg/L seawater (Appendix B).
3.1.2.10 Prasinoxanthin
The maximum levels of prasinoxanthin concentration were in June and August (Figure 24).
Figure 24. Prasinoxanthin.
Average concentration of prasinoxanthin during each month of 2010, given as mg/L seawater (Appendix B).
34 3.1.2.11 Lutein
A maximum concentration level of lutein was in August, but smaller peak-levels are also measured in January-February and in June (Figure 25).
Figure 25. Lutein.
Average concentration of lutein during each month of 2010, given as mg/L seawater (Appendix B).
3.1.2.12 Violaxanthin and antheraxanthin
The pigments violaxanthin and antheraxanthin only present in the seawater samples from May to August (Figure 26).
The concentration curve of violaxanthin divides in two dominant peaks, one in May-June, and one in August (Figure 26; left scheme). Top single day result was on May 19 measuring
~0.106 mg/L seawater (Appendix B).
Antheraxanthin concentration was measured at monthly concentrations between 0.034 and 0.035 mg/L seawater. The period of highest concentration levels was in August (Figure 26;
right). Summarized the major peaks was in May going on a larger combined peak in August.
35
Figure 26. Violaxanthin and antheraxanthin.
Average concentration of violaxanthin and antheraxanthin during each month of 2010, given as mg/L seawater (Appendix B).
3.2 Hydrographic SD-measurements
In order to study possible influences between the parameters on the amount of pigments in the water samples, the different parameters measured and presented are salinity, water
temperature, amount of oxygen and fluorescence in the water column.
36 3.2.1 Fluorescence
Estimated concentration fluorescence in the water column, shows distinct top measurements in June, with a steep increase from April-May (Figure 26). The curve then shows a gradually decrease onwards the rest of the summer and onto autumn.
A steep increase in measured values at June 4 measuring 10~ µg/L, with another distinct high July 19 measuring ~4 µg /L s.w. (Appendix A). Highest concentration of fluorescence was from May onto beginning of October. Lowest estimated amounts in January to the beginning of March varying from 0.2 to 0.4 µ/L, and further on a slow decrease from October towards the late autumn.
Figure 27. Fluorescence.
Estimated concentration of fluorescence in micrograms per litre of seawater (µg /L s.w.) (Appendix A).
3.2.2 Seawater temperature
The seawater temperature varied in an uneven curve, declining in mid-winter and rising as the illumination caused by increased length of diurnal daylight, combined with increased earth- crust heat radiation (Figure 28).
Seawater temperature varied through the period from 3.1oC in February 19 up to a maximum at 10.4oC on August 6. The average temperature in the water column started rising above 5oC in the beginning of May onto a downfall below 5oC in November. Maximal seawater
temperature was in July-August. In the beginning of May, the temperature exceeded 5oC
37 (Figure 28; appendix A). Note that the measurements from February 17 at 9.6oC, have been removed from the scheme.
Figure 28.Seawater temperature.
Seawater temperature in the water column based on probe measurements, given in degrees Celsius ( oC) (Appendix A).
3.2.3 Oxygen
Measurements of oxygen indicates variations from 8 mg/l or 80 % and upwards. There are two main periods with noticeable variations (Figure 29). The oxygen levels dropped
remarkably in the period from April 16 to April 19.This occurs after a period of rising values on April 12. After the oxygen dropped, on April 23 and 26 (Appendix A).
Figure 29.Oxygen levels.
Average levels of oxygen in the water column over the year 2010. The measurements are given as milligrams per litre seawater (mg/L s.w.) and percent (%) saturation, calculated from values in the entire water column
(Appendix A).
0 20 40 60 80 100
0 2 4 6 8 10 12 14
% O2
O2mg/L
Oxsygen mg/L % Oxygen
38 3.2.4 Salinity and density
The schemes in figure 30 and 31, show probe measurements for density and salinity vary through the year. Calculations of standard derivation estimates that average levels of salinity vary from S ≈ 30 to S ≈ 33. It was found low levels of salinity, in various degrees, and in varying in amplitudes, the fluctuations in May-September and in November (figure 30, left).
Most of the values from the density measurements are in the area between approximately 26.5 and 23.5 g/ml.
Figure 30.Salinity and density.
Seasonal average measurements, including calculated standard derivations, of Salinity (S ≈), (left) and water column density (Dn ~ g/ml) (right) (from SD-Probe measurements).
39 Salinity and density levels seem to have relatively correlating values, through the year. One specific difference was in the beginning of March, with a sudden increased density, but no effect on the salinity was found. This occurred on March 5 (Figure 31 and Appendix A).
Figure 31.Salinity and density.
Seasonal average measurements of Salinity (S ≈) and density (Dn g/ml), combined values through the year 2010 (Appendix A).
20 22 24 26 28 30
20 22 24 26 28 30 32 34 36
Dn
≈ S
Salinity and density
Salinity Density
